A multi-stage low-carbon planning method and system for a load park

Abstract

The present invention discloses a multi-stage low-carbon planning method and system for a load park. The method comprises: constructing a data center energy consumption model according to the load spatiotemporal transfer characteristics of the data center of the load park, and constructing a resource energy consumption model according to the power resources of the load park; calculating the carbon emissions according to the carbon emission rights quota of the load park, and calculating the economic benefits of the carbon trading market according to the carbon emission rights quota and the carbon emissions; constructing a multi-stage low-carbon planning model according to the data center energy consumption model, the resource energy consumption model, the carbon emissions of the load park and the economic benefits of the carbon trading market, and taking the minimization of the multi-stage comprehensive cost as the objective function; optimally solving the multi-stage low-carbon planning model to obtain a planning and construction plan; this embodiment realizes the investment and construction of corresponding low-carbon transformation computing power and power resources in different planning stages of the load park, improves the resource utilization efficiency of the park, and reduces the carbon emissions of the park operation.

Description

Technical Field

[0001] The present invention relates to the field of power resource planning, and in particular to a multi-stage low-carbon planning method and system for a load park. Background Art

[0002] With the vigorous development of the new generation of information technology of "cloud, big data, Internet of Things, mobile and chain", the number of data centers as the physical carrier of information systems has increased rapidly, and the volume and scale of new load parks with data centers as the main load have continued to expand. At present, the power consumption and carbon emissions of new load parks are relatively large. Under the background of green and low-carbon development, how to carry out multi-stage low-carbon planning for new load parks has become a hot topic of research. At the same time, the load of data centers has unique time and space transfer characteristics, which makes new load parks have huge demand response potential. How to make good use of the flexible adjustment ability of data center loads in the planning stage and carry out low-carbon planning for new load parks is the focus of research.

[0003] At present, some scholars have conducted research on new load parks, but they generally focus on new load operation optimization, comprehensive energy planning and other aspects. There has been no research on the multi-stage planning of new load parks, nor has it considered carbon emission constraints in the planning. Low-carbon planning is not applied to new load parks. As a result, in the case of single energy planning, the impact of carbon emissions cannot be comprehensively considered, and it is impossible to ensure that the carbon emissions of the actual operation of the park meet the standards. Summary of the invention

[0004] The present invention provides a multi-stage low-carbon planning method and system for a load park, which enables the investment and construction of corresponding low-carbon transformation computing power and power resources in different planning stages of the load park, improves the resource utilization efficiency of the park, and reduces the carbon emissions of the park operation.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a new multi-stage low-carbon planning method for a load park, comprising:

[0006] According to the load time-space transfer characteristics of the data center in the new load park, a data center energy consumption model is constructed, and according to the power resources of the new load park, a resource energy consumption model is constructed; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model;

[0007] Calculate the carbon emissions of new load parks based on their carbon emission quotas, and calculate the economic benefits of the carbon trading market based on their carbon emission quotas and carbon emissions;

[0008] Based on the data center energy consumption model, resource energy consumption model, carbon emissions of new load parks and economic benefits of the carbon trading market, and taking the minimization of multi-stage comprehensive costs as the objective function, a multi-stage low-carbon planning model is constructed;

[0009] The multi-stage low-carbon planning model is optimally solved to obtain a planning investment and construction plan, which includes computing power and power resources invested and constructed in different planning stages.

[0010] In implementing the embodiment of the present invention, a data center energy consumption model is constructed according to the load spatiotemporal transfer characteristics of the data center of the new load park, and a resource energy consumption model is constructed according to the power resources of the new load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model; the carbon emissions of the new load park are calculated according to the carbon emission rights quota of the new load park, and the economic benefits of the carbon trading market are calculated according to the carbon emission rights quota and carbon emissions of the new load park; according to the data center energy consumption model, the resource energy consumption model, the carbon emissions of the new load park and the economic benefits of the carbon trading market, and with the minimization of the multi-stage comprehensive cost as the objective function, a multi-stage low-carbon planning model is constructed; the multi-stage low-carbon planning model is optimally solved to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages. A multi-stage low-carbon planning method for a new type of load park considering the temporal and spatial transfer characteristics of data center loads is proposed. The temporal and spatial transfer characteristics of data center loads are studied, and energy consumption models and carbon emission models of computing power and power resources are constructed. Combined with the constraints of carbon emission reduction targets, a multi-stage green and low-carbon planning model for a new type of load park with the goal of minimizing comprehensive costs is constructed, namely, a multi-stage low-carbon planning model. The multi-stage low-carbon planning model is solved to obtain a planning and construction plan. The corresponding low-carbon transformation computing power and power resources are invested and constructed in different planning stages of the new type of load park, which has a certain reference role for planners of the new type of load park, improves the resource utilization efficiency of the park, and reduces the carbon emissions of the park operation.

[0011] As a preferred solution, a data center energy consumption model is constructed based on the load time-space transfer characteristics of the data center in the new load park, specifically:

[0012] According to the number of task requests transferred in time and space by the data center of the new load park, the task request of the data center is obtained, which is specifically:

[0013]

[0014] Among them, v i,n,s,t represents the task request of data center i at time t in the sth scenario of the nth planning year, c i,n,s,t represents the number of delay-sensitive task requests of data center i at time t in the sth scenario of the nth planning year, d i,n,s,t represents the number of delay-tolerant task requests in data center i at time t in scenario s in planning year n, and represents the number of delay-tolerant task requests that decrease and increase in data center i at time t in the sth scenario in the nth planning year, w i|i′,n,s,t represents the number of task requests transferred from data center i to data center iˊ at time t in the sth scenario of the nth planning year, and I represents the total number of data centers;

[0015] According to the conditions satisfied by the task request of the data center during the time-space transfer, the operation constraints of the data center are obtained; wherein the operation constraints of the data center include the maximum value constraint of active servers, the maximum response time constraint and the maximum transmission capacity constraint;

[0016] According to the server's no-load power, the server's full-load power, the number of servers, the data center's task requests and operation constraints, a data center server linear model is constructed, specifically:

[0017]

[0018] in, P represents the server power of data center i at time t in the sth scenario in the nth planning year, n Indicates the no-load power of the server, P p Indicates the full load power of the server, m i,n,s,t represents the number of active servers in data center i at time t in scenario s in planning year n, and F is the maximum number of tasks processed by a single server;

[0019] According to the linear model of the data center server and the energy efficiency coefficient of the data center, a data center energy consumption model is constructed, which is as follows:

[0020]

[0021] in, It represents the total power of data center i at the time t of the sth scenario in the nth planning year, and PUE represents the energy efficiency coefficient of the data center.

[0022] As a preferred solution, according to the conditions satisfied by the task request of the data center during the time-space transfer, the operation constraints of the data center are obtained, specifically:

[0023] Based on the assumption that the number of active servers does not exceed the number of servers built, a maximum active server limit is established, specifically:

[0024] m i,n,s,t ≤X SRV,n

[0025] Among them, X SRV,n The number of servers built for data center i in the nth planning year;

[0026] According to the task request of the data center, the maximum response time constraint is constructed within the maximum response time, which is as follows:

[0027] 0≤1 / (m i,n,s,t F i,n,s,t )≤D

[0028] Where D represents the delay limit of delay-sensitive load;

[0029] According to the principle that the transfer of task requests between different data centers does not exceed the maximum communication transmission capacity between data centers, a maximum transmission capacity constraint is constructed, which is as follows:

[0030] -w i|i′,max ≤w i|i′,s,t ≤w i|i′,max

[0031] Among them, w i|i′,max It represents the maximum number of task requests that can be transferred from data center i to data center iˊ.

[0032] As a preferred solution, a resource energy consumption model is constructed based on the power resources of the new load park, specifically:

[0033] According to the power generation and ramp power of the thermal power unit, a thermal power unit model is constructed, specifically:

[0034]

[0035]

[0036] in, represents the power generation cost of the thermal power unit at the sth scenario at time t in the nth planning year, α represents the power generation cost per unit power, represents the power generation capacity of the thermal power unit at the time t in the sth scenario in the nth planning year, and Indicates the upper and lower limits of the power generation capacity of the thermal power unit. and Indicates the upper and lower limits of the climbing power of the thermal power unit;

[0037] According to the power and amount of energy storage, the energy storage model is constructed, specifically:

[0038]

[0039] In the formula, represents the operating cost of energy storage at the sth scenario at time t in the nth planning year, β represents the operating cost per unit power of energy storage, represents the power of energy storage at the time t in the sth scenario in the nth planning year, represents the amount of energy storage at the sth scenario at time t in the nth planning year, Δt represents the scheduling step, and Respectively represent the upper and lower limits of energy storage power, and Respectively represent the upper and lower limits of energy storage capacity;

[0040] According to the power of the renewable energy generator set, a renewable energy generator set model is constructed, specifically:

[0041]

[0042] in, represents the power of the renewable energy generator at the sth scenario at time t in the nth planning year, and They respectively represent the upper and lower limits of the power of renewable energy generators.

[0043] As a preferred solution, the carbon emissions of the new load park are calculated according to the carbon emission quota of the new load park, specifically:

[0044] According to the power generation power of thermal power units, energy storage power, and total power of data centers, the carbon emission quota of new load parks is calculated as follows:

[0045]

[0046] Among them, E′ Park,n , E e ' ,buy,n , E′ MT,n , E′ ESS,n and E′ DC,n They represent the carbon emission quotas of new load parks, external power grid power purchases, thermal power units, energy storage and data centers in the nth planning year; δ e ′,δ′ MT ,δ′ ESS and δ′ DC They represent the carbon emission quotas obtained by the power unit purchased by the upper power grid, the power unit produced by the thermal power unit, the power unit of energy storage, and the power unit of the data center respectively; It represents the power purchased from the power grid by the new load park at time t in the sth scenario in the nth planning year; It represents the power generation capacity of the thermal power unit at the time t of the sth scenario in the nth planning year; represents the power of energy storage at time t in the sth scenario in the nth planning year; represents the total power of data center i at time t in the sth scenario in the nth planning year;

[0047] According to the carbon emission quota of the new load park, the carbon emissions of the new load park are calculated as follows:

[0048]

[0049] Among them, E Park,n , E e,buy,n , E MT,n , E ESS,n and E DC,n They represent the carbon emissions of new load parks, external power grid power purchases, thermal power units, energy storage and data centers in the nth planning year; δ e , δ MT , δ ESS and δ DC They respectively represent the unit power of electricity purchased from the superior power grid, the unit power produced by the thermal power unit, the unit power of energy storage, and the carbon emission quota obtained per unit power of the data center.

[0050] As a preferred solution, the economic benefits of the carbon trading market are calculated based on the carbon emission quota and carbon emissions of the new load park, specifically:

[0051] According to the carbon emission quota and carbon emissions of the new load park, the carbon emission quota of the carbon trading market is calculated as follows:

[0052] E tr,n =E Park,n -E′ Park,n

[0053] Among them, E tr,n It represents the carbon emission quota of the carbon trading market in the nth planning year;

[0054] The economic benefits of the carbon trading market are calculated based on the carbon emission quota, carbon trading base price, price growth rate and unit carbon emission interval length of the carbon trading market, specifically:

[0055]

[0056] Among them, C CO2,n is the economic benefit of the carbon trading market in the nth planned year, λ is the carbon trading base price, α is the price growth rate, and d is the length of the unit carbon emission interval.

[0057] As a preferred solution, based on the data center energy consumption model, resource energy consumption model, carbon emissions of new load parks and economic benefits of the carbon trading market, and taking the minimization of multi-stage comprehensive costs as the objective function, a multi-stage low-carbon planning model is constructed, specifically:

[0058] The data center energy consumption model and resource energy consumption model are used as the operating costs of the new load park, specifically:

[0059]

[0060] Among them, C ope,n represents the operating cost of the new load park in the nth planning year, c e,n,s,t It represents the electricity purchase cost of the upper power grid at the t time in the sth scenario in the nth planning year;

[0061] According to the equipment investment capacity and the cost per unit capacity, the equipment construction cost is obtained, which is:

[0062]

[0063] Among them, C constr,k represents the equipment construction cost in the kth planning stage; m represents the equipment type, M represents the equipment type set, and C m,k represents the cost of investing in equipment m per unit capacity in the kth planning stage, X m,k represents the construction variable of equipment m in the kth planning stage, N m,k It represents the construction capacity of equipment m in the kth planning stage;

[0064] Taking the operating cost, economic benefits of the carbon trading market and equipment construction cost as the multi-stage comprehensive cost, taking the minimum multi-stage comprehensive cost as the objective function, taking the carbon emission constraint, operating constraint and equipment construction quantity constraint as the planning constraint conditions, a multi-stage low-carbon planning model is constructed. Specifically:

[0065]

[0066] R n =(1+r) -n

[0067] Among them, L RV A represents the residual value of the equipment at the end of the planning period. k is the starting year of the kth planning stage, N is the total planning period, K is the number of planning stages, r is the discount rate, R n and R N Respectively represent the A k The discount factors for the planning year, the nth planning year and the Nth planning year, where n represents the year;

[0068] Among them, the residual value of the equipment at the end of the planning period is calculated based on the annual depreciation cost of the equipment, the equipment construction cost and the annual depreciation cost of the equipment, specifically:

[0069] c dep,j =c constr,j (1-δ j ) / N j

[0070]

[0071] Among them, M x is the number of remaining equipment at the end of the planning period, c constr,j represents the construction cost of the jth equipment, T j is the number of years of use of the jth equipment among the remaining equipment at the end of the planning period, c dep,j is the annual depreciation cost of the jth equipment, δ j is the net residual value rate of the jth equipment, N j is the life span of the jth device.

[0072] As the preferred solution, carbon emission constraints, operation constraints and equipment construction quantity constraints are used as planning constraints, specifically:

[0073] According to the planned capacity limit of energy equipment in the new load park, the equipment construction quantity constraints are established, specifically:

[0074]

[0075] Among them, N m,k Plan the capacity of m types of energy equipment for the kth planning stage of the new load park, Plan the capacity upper limit for type m energy equipment in the kth planning stage of the new load park;

[0076] Construct operation constraints based on the equipment operation constraints that the thermal power units, energy storage, renewable energy generators, and data center servers in the new load park need to meet;

[0077] According to the upper limit of carbon emissions of new load parks, carbon emission constraints are established, specifically:

[0078]

[0079] Among them, E Park,n represents the carbon emissions of the new load park in the nth planning year; E max,n It represents the upper limit of carbon emissions of the new load park in the nth planning year;

[0080] Equipment construction quantity constraints, operation constraints and carbon emission constraints are used as planning constraints.

[0081] As a preferred solution, the multi-stage low-carbon planning model is optimally solved to obtain the planning and construction plan, which is as follows:

[0082] Auxiliary variables are established, and based on the auxiliary variables, the economic benefits of the carbon trading market in the multi-stage low-carbon planning model are linearly transformed, so that the multi-stage low-carbon planning model is transformed into a mixed integer programming model; the linear transformation formula is:

[0083] C CO2,n =y1λE tr,n +y2λ(1+α)(E tr,n -d)+λd+y3λ(1+2α)(E tr,n -2d)+λ(2+α)d

[0084] +y4λ(1+3α)(E tr,n -3d)+λ(3+3α)d+y5λ(1+4α)(E tr,n -4d)+λ(4+6α)d

[0085] +y6λ(1+5α)(E tr -5d)+λ(5+10α)d

[0086] Among them, y1, y2, y3, y4, y5 and y6 are auxiliary variables, y1+y2+y3+y4+y5=1;

[0087] According to the objective function and planning constraints, the mixed integer programming model is optimally solved through the solver to obtain the planning investment and construction plan.

[0088] In order to solve the same technical problem, an embodiment of the present invention further provides a multi-stage low-carbon planning system for a new type of load park, including: the multi-stage low-carbon planning system for a new type of load park is used to implement a multi-stage low-carbon planning method for a new type of load park, including: an energy consumption model building module, a carbon emission calculation module, a planning model building module and a model solving module;

[0089] Among them, the energy consumption model building module is used to build a data center energy consumption model according to the load time and space transfer characteristics of the data center of the new load park, and to build a resource energy consumption model according to the power resources of the new load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model;

[0090] The carbon emission calculation module is used to calculate the carbon emissions of the new load park according to the carbon emission quota of the new load park, and calculate the economic benefits of the carbon trading market according to the carbon emission quota and carbon emissions of the new load park;

[0091] The planning model building module is used to build a multi-stage low-carbon planning model based on the data center energy consumption model, resource energy consumption model, carbon emissions of new load parks and economic benefits of the carbon trading market, and with the minimum multi-stage comprehensive cost as the objective function;

[0092] The model solving module is used to optimally solve the multi-stage low-carbon planning model and obtain a planning investment and construction plan; the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 : A flow chart of an embodiment of a multi-stage low-carbon planning method for a new type of load park provided by the present invention;

[0094] Figure 2 : A low-carbon planning path diagram of an embodiment of a multi-stage low-carbon planning method for a new type of load park provided by the present invention;

[0095] Figure 3 : A flow chart of an embodiment of a multi-stage low-carbon planning system for a new type of load park provided by the present invention. DETAILED DESCRIPTION

[0096] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] Embodiment 1

[0098] Please refer to Figure 1 , which is a flow chart of a multi-stage low-carbon planning method for a new type of load park provided by an embodiment of the present invention. The multi-stage low-carbon planning method of this embodiment is applicable to a new type of load park. This embodiment constructs and solves a multi-stage low-carbon planning model, and the new type of load park invests in corresponding low-carbon transformation computing power and power resources at different planning stages to improve the resource utilization efficiency of the park and reduce the carbon emissions of the park operation. The multi-stage low-carbon planning method includes steps 101 to 104, and each step is as follows:

[0099] Step 101: construct a data center energy consumption model based on the load time-space transfer characteristics of the data center of the new load park, and construct a resource energy consumption model based on the power resources of the new load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model.

[0100] In this embodiment, the time-space transfer characteristics of the data center in the new load park are fully considered, and the multi-stage green and low-carbon planning path of the new load park is studied in combination with the carbon emission reduction target. The low-carbon planning path is as follows: Figure 2As shown in the figure, the load spatiotemporal transfer characteristics and energy consumption model of the new load park data center are studied, and energy consumption models of other power resources in the new load park are constructed, including energy consumption models of thermal power units, energy storage, and renewable energy power generation units. The carbon emissions of the new load park are calculated, and the economic benefits of the carbon trading market of the new load park are studied. A multi-stage green and low-carbon plan for the new load park with the goal of minimizing the comprehensive cost is constructed, and the load spatiotemporal transfer characteristics and energy consumption model of the new load park studied and the energy consumption model of other power resources are considered in the operating cost of the objective function. The carbon emissions calculation method is used in the carbon market benefits of the objective function to determine the computing power and power resources invested in different planning stages, providing technical support for the green and low-carbon development of the new load park.

[0101] It should be noted that the power consumption of data centers is mainly generated by servers. Servers consume power when processing various types of task requests. Among them, task requests can be divided into delay-tolerant task requests and delay-sensitive task requests according to the user's requirements for the execution time of task requests. Delay-tolerant task requests usually include image processing, data backup, etc., which have low requirements for execution timeliness, can tolerate large delays, have time scheduling potential, and can achieve time transfer of power load by adjusting the task request processing time. Delay-sensitive task requests include interactive information, real-time transactions, online contacts, etc., which have high requirements for processing time and generally need to be executed immediately, and cannot be scheduled in time sequence. At the same time, different data centers can communicate with each other through optical fibers and other means, thereby transferring task requests and realizing spatial transfer of power load.

[0102] Optionally, according to the load spatiotemporal transfer characteristics of the data center of the new load park, a data center energy consumption model is constructed, including steps 11-14 specifically as follows:

[0103] Step 11: According to the number of task requests transferred in time and space by the data center of the new load park, the task request of the data center is obtained, which is specifically:

[0104]

[0105] Among them, v i,n,s,t represents the task request of data center i at time t in the sth scenario of the nth planning year, c i,n,s,t represents the number of delay-sensitive task requests of data center i at time t in the sth scenario of the nth planning year, d i,n,s,t represents the number of delay-tolerant task requests in data center i at time t in scenario s in planning year n, and represents the number of delay-tolerant task requests that decrease and increase in data center i at time t in the sth scenario in the nth planning year, w i|i′,n,s,trepresents the number of task requests transferred from data center i to data center iˊ at time t in the sth scenario of the nth planning year, and I represents the total number of data centers;

[0106] In this embodiment, the time-space transfer of data center load is mainly achieved by transferring task requests in time and space. Therefore, the task request of the data center at a certain time in a certain scenario can be determined based on the time-space transfer characteristics of the load.

[0107] Step 12: According to the conditions satisfied by the task request of the data center during the time-space transfer, the operation constraints of the data center are obtained; wherein the operation constraints of the data center include the maximum value constraint of active servers, the maximum response time constraint and the maximum transmission capacity constraint;

[0108] In this embodiment, when the task request of the data center is transferred in time and space, it needs to meet some operation constraints, such as the maximum value constraint of active servers, the maximum response time constraint and the maximum transmission capacity constraint. These constraints constitute the operation constraints of the data center.

[0109] Optionally, step 12 is specifically: according to the number of active servers not exceeding the number of servers to be built, a maximum active server constraint is constructed, specifically:

[0110] m i,n,s,t ≤X SRV,n (2)

[0111] Among them, m i,n,s,t represents the number of active servers in data center i at time t in scenario s in planning year n, X SRV,n The number of servers built for data center i in the nth planning year.

[0112] In this embodiment, the server control of the data center adopts dynamic cluster server configuration (DCSC), that is, under the condition of ensuring sufficient task request processing capacity, only a minimum number of servers are active, and other servers enter a dormant state to reduce the overall server energy consumption. The number of active servers cannot exceed the construction number, thereby constructing the maximum active server constraint.

[0113] Optionally, a maximum response time constraint is constructed based on the task request of the data center being executed within the maximum response time, specifically:

[0114] 0≤1 / (m i,n,s,t F i,n,s,t )≤D (3)

[0115] Where D represents the delay limit of delay-sensitive load; F is the maximum number of tasks processed by a single server.

[0116] In this embodiment, the data center needs to ensure that the task request is executed within the maximum response time, and calculates the waiting time of the task request according to the M / M / 1 queuing model. Thus, the maximum response time constraint is constructed based on the task request of the data center being executed within the maximum response time.

[0117] Optionally, according to the fact that the transfer of task requests between different data centers does not exceed the maximum communication transmission capacity between data centers, a maximum transmission capacity constraint is constructed, specifically:

[0118] -w i|i′,max ≤w i|i′,s,t ≤w i|i′,max (4)

[0119] Among them, w i|i′,max It represents the maximum number of task requests that can be transferred from data center i to data center iˊ.

[0120] In this embodiment, the transfer of task requests between different data centers cannot exceed the maximum communication transmission capacity between the data centers, thereby constructing a maximum transmission capacity constraint.

[0121] Step 13: According to the server's no-load power, the server's full-load power, the number of servers, the data center's task requests, and the operation constraints, a linear model of the data center server is constructed. Specifically,

[0122]

[0123] in, P represents the server power of data center i at time t in the sth scenario in the nth planning year, n Indicates the no-load power of the server, P p Indicates the full load power of the server, m i,n,s,t represents the number of active servers in data center i at time t in scenario s in planning year n, and F is the maximum number of tasks processed by a single server;

[0124] Step 14: Based on the data center server linear model and the data center energy efficiency coefficient, a data center energy consumption model is constructed, specifically:

[0125]

[0126] in, It represents the total power of data center i at the time t of the sth scenario in the nth planning year, and PUE represents the energy efficiency coefficient of the data center.

[0127] In this embodiment, the data center cooling power consumption and other power consumption such as the power distribution system can be regarded as a linear function of the server power consumption. Therefore, according to the relationship between the overall power consumption of the data center and the server power consumption, a data center energy consumption model can be constructed.

[0128] In this embodiment, the new load park considered includes four types of resources: thermal power units, energy storage, data centers, and renewable energy power generation units. After constructing the energy consumption model of the data center, the models of other power resources are continuously constructed.

[0129] Optionally, a resource energy consumption model is constructed based on the power resources of the new load park, specifically:

[0130] According to the power generation and ramp power of the thermal power unit, a thermal power unit model is constructed, specifically:

[0131]

[0132] in, represents the power generation cost of the thermal power unit at the sth scenario at time t in the nth planning year, α represents the power generation cost per unit power, represents the power generation capacity of the thermal power unit at the time t in the sth scenario in the nth planning year, and Indicates the upper and lower limits of the power generation capacity of the thermal power unit. and Indicates the upper and lower limits of the climbing power of the thermal power unit;

[0133] According to the power and amount of energy storage, the energy storage model is constructed, specifically:

[0134]

[0135] In the formula, represents the operating cost of energy storage at the sth scenario at time t in the nth planning year, β represents the operating cost per unit power of energy storage, represents the power of energy storage at the time t in the sth scenario in the nth planning year, represents the amount of energy storage at the sth scenario at time t in the nth planning year, Δt represents the scheduling step, and Respectively represent the upper and lower limits of energy storage power, and Respectively represent the upper and lower limits of energy storage capacity;

[0136] According to the power of the renewable energy generator set, a renewable energy generator set model is constructed, specifically:

[0137]

[0138] in, represents the power of the renewable energy generator at the sth scenario at time t in the nth planning year, and They respectively represent the upper and lower limits of the power of renewable energy generators.

[0139] In this embodiment, renewable energy refers to wind power and photovoltaic power, and the power generation costs of both are almost zero. Therefore, the present invention ignores the power generation cost of renewable energy, but must obey the operation constraints of renewable energy. Formula (14) is the operation constraints of renewable energy.

[0140] Step 102: Calculate the carbon emissions of the new load park based on the carbon emission rights quota of the new load park, and calculate the economic benefits of the carbon trading market based on the carbon emission rights quota and carbon emissions of the new load park.

[0141] In this embodiment, the carbon emissions of the new load park are calculated, and the economic benefits of the carbon trading market of the new load park are studied. Under the carbon trading market mechanism, the new load park has a certain carbon emission quota. If the actual carbon emissions of the new load park are less than the carbon emission quota, the excess carbon quota can be sold in the carbon trading market for profit. Otherwise, it is necessary to purchase carbon quotas from the carbon trading market.

[0142] Optionally, the carbon emissions of the new load park are calculated based on the carbon emission quota of the new load park, specifically:

[0143] According to the power generation power of thermal power units, energy storage power, and total power of data centers, the carbon emission quota of new load parks is calculated as follows:

[0144]

[0145] Among them, E′ Park,n , E e ' ,buy,n , E′ MT,n , E′ ESS,n and E′ DC,n They represent the carbon emission quotas of new load parks, external power grid power purchases, thermal power units, energy storage and data centers in the nth planning year; δ e ′,δ′ MT ,δ′ ESS and δ′ DC They represent the unit power of electricity purchased by the upper-level power grid, the unit power of electricity produced by the thermal power unit, the unit power of energy storage (unit power of energy storage and unit thermal power of production), and the carbon emission quota obtained by the unit power of the data center; It represents the power purchased from the power grid by the new load park at time t in the sth scenario in the nth planning year; It represents the power generation capacity of the thermal power unit at the time t of the sth scenario in the nth planning year; represents the power of energy storage at time t in the sth scenario in the nth planning year; represents the total power of data center i at time t in the sth scenario in the nth planning year;

[0146] According to the carbon emission quota of the new load park, the carbon emissions of the new load park are calculated as follows:

[0147]

[0148] Among them, E Park,n , E e,buy,n , E MT,n , E ESS,n and E DC,n They represent the carbon emissions of the new load park, external power grid power purchase, thermal power units, energy storage and data center in the nth planning year, that is, the actual carbon emissions; δ e , δ MT , δ ESS and δ DC They respectively represent the unit power of electricity purchased by the superior power grid, the unit power of electricity produced by the thermal power unit, the unit power of energy storage (unit power of energy storage and unit thermal power of production), and the carbon emission quota obtained by the unit power of the data center.

[0149] Optionally, the economic benefits of the carbon trading market are calculated based on the carbon emission quota and carbon emissions of the new load park, specifically:

[0150] According to the carbon emission quota and carbon emissions of the new load park, the carbon emission quota of the carbon trading market is calculated as follows:

[0151] E tr,n =E Park,n -E′ Park,n (17)

[0152] Among them, E tr,n It represents the carbon emission quota of the carbon trading market in the nth planning year, that is, the carbon emission quota that the comprehensive energy system can trade in the carbon trading market in the nth planning year;

[0153] In this embodiment, the integrated energy system can purchase or sell the number of carbon emission quotas on the carbon trading market.

[0154] The economic benefits of the carbon trading market are calculated based on the carbon emission quota, carbon trading base price, price growth rate and unit carbon emission interval length of the carbon trading market, specifically:

[0155]

[0156] Among them, C CO2,n is the economic benefit of the carbon trading market in the nth planned year, λ is the carbon trading base price, α is the price growth rate, and d is the length of the unit carbon emission interval.

[0157] In this embodiment, a tiered carbon trading mechanism is adopted to set multiple price ranges. When the integrated energy system needs to purchase more carbon emission quotas, the corresponding range price will be higher, thereby further limiting carbon emissions.

[0158] Step 103: Based on the data center energy consumption model, resource energy consumption model, carbon emissions of new load parks and economic benefits of the carbon trading market, and taking the minimization of multi-stage comprehensive costs as the objective function, a multi-stage low-carbon planning model is constructed.

[0159] In this embodiment, a multi-stage green low-carbon planning model for a new load park is constructed with the goal of minimizing the comprehensive cost, that is, a multi-stage low-carbon planning model. Since the load growth of the new load park is uncertain, for a plan of more than 10 years, if a single-stage plan is adopted, it is easy to cause construction redundancy or insufficient construction capacity due to inaccurate load forecasting, and even affect the normal operation of the new load park. Therefore, for long-term planning, the use of a multi-stage planning method can effectively reduce planning and construction risks.

[0160] Optionally, step 103 specifically includes step 1031 to step 1033, each step is specifically as follows:

[0161] Step 1031: The data center energy consumption model and the resource energy consumption model are used as the operating cost of the new load park, specifically:

[0162]

[0163] Among them, C ope,n represents the operating cost of the new load park in the nth planning year, c e,n,s,t It represents the electricity purchase cost of the upper power grid at the t time in the sth scenario in the nth planning year;

[0164] Step 1032: According to the equipment investment capacity and the cost per unit capacity, the equipment construction cost is obtained, which is specifically:

[0165]

[0166] Among them, C constr,k represents the equipment construction cost of the kth planning stage; m represents the equipment type, M represents the equipment type set, M = {MT, RNW, ESS, SRV}, C m,k represents the cost of investing in equipment m per unit capacity in the kth planning stage, X m,kIndicates the construction variable of equipment m in the kth planning stage, that is, a 0-1 variable, indicating whether equipment m is constructed in the kth planning stage, N m,k It represents the construction capacity of equipment m in the kth planning stage;

[0167] Optionally, the residual value of the equipment at the end of the planning period is calculated based on the annual depreciation cost of the equipment, the equipment construction cost and the annual depreciation cost of the equipment, specifically:

[0168] c dep,j =c constr,j (1-δ j ) / N j (twenty one)

[0169]

[0170] Among them, M x is the number of remaining equipment at the end of the planning period, c constr,j represents the construction cost of the jth equipment, T j is the number of years of use of the jth equipment among the remaining equipment at the end of the planning period, c dep,j is the annual depreciation cost of the jth equipment, δ j is the net residual value rate of the jth equipment, N j is the life span of the jth device.

[0171] In this embodiment, some energy equipment has not reached the retirement age at the end of the planning period. Therefore, in order to measure the economic feasibility of the planning scheme more scientifically and reasonably, it is necessary to calculate the residual value of some energy equipment that has not been retired after depreciation.

[0172] Step 1033: Taking the operation cost, economic benefits of the carbon trading market and equipment construction cost as the multi-stage comprehensive cost, taking the minimization of the multi-stage comprehensive cost as the objective function, taking the carbon emission constraint, operation constraint and equipment construction quantity constraint as the planning constraint conditions, a multi-stage low-carbon planning model is constructed, specifically:

[0173]

[0174] R n =(1+r) -n (twenty four)

[0175] Among them, C constr,k is the equipment construction cost in the kth planning stage, C ope,n , C CO2,n are the operating cost of the new load park and the carbon market income (economic income of the carbon trading market) in the nth planning year, L RV A represents the residual value of the equipment at the end of the planning period. kis the starting year of the kth planning stage, N is the total planning period, K is the number of planning stages, r is the discount rate, R n and R N Respectively represent the A k The discount factor for the planning year, the nth planning year and the Nth planning year, where n represents the year, from 1 to N;

[0176] In this embodiment, the equipment considered for planning includes thermal power units, renewable energy power units, data center servers, and energy storage, and a planning model for an integrated energy system is constructed with the goal of minimizing the multi-stage comprehensive cost. The comprehensive cost includes construction cost, operation cost, and carbon market income (economic income from the carbon trading market).

[0177] Optionally, carbon emission constraints, operation constraints and equipment construction quantity constraints are used as planning constraints, specifically including steps 31 to 34:

[0178] Step 31: According to the planned capacity limit of energy equipment in the new load park, establish equipment construction quantity constraints, specifically:

[0179]

[0180] Among them, N m,k Plan the capacity of m types of energy equipment for the kth planning stage of the new load park, Plan the capacity upper limit for type m energy equipment in the kth planning stage of the new load park;

[0181] In this embodiment, due to factors such as the floor area and construction funds of the new load park, there is an upper limit on the number of equipment to be built. Therefore, a constraint on the number of equipment to be built is constructed based on the planned capacity upper limit of the energy equipment in the new load park.

[0182] Step 32: Construct operation constraints according to the equipment operation constraints that the thermal power units, energy storage, renewable energy generators and data center servers of the new load park need to meet;

[0183] In this embodiment, the thermal power units, energy storage, new energy (renewable energy power generation units) and data center servers of the new load park need to meet certain operating constraints, namely, equations (1) to (14).

[0184] Step 33: Construct carbon emission constraints based on the upper limit of carbon emissions of the new load park, specifically:

[0185]

[0186] Among them, E Park,n represents the carbon emissions of the new load park in the nth planning year; Emax,n It represents the upper limit of carbon emissions of the new load park in the nth planning year;

[0187] In this embodiment, the carbon emissions of the new load park cannot exceed the maximum value. Therefore, a carbon emission constraint is constructed based on the upper limit of the carbon emissions of the new load park.

[0188] Step 34: Use equipment construction quantity constraints, operation constraints and carbon emission constraints as planning constraints.

[0189] Step 104: Optimally solve the multi-stage low-carbon planning model to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages.

[0190] Optionally, step 104 specifically includes step 1041 to step 1042, and each step is specifically as follows:

[0191] Step 1041: establish auxiliary variables, and according to the auxiliary variables, perform linear transformation on the economic benefits of the carbon trading market in the multi-stage low-carbon planning model, so that the multi-stage low-carbon planning model is transformed into a mixed integer programming model; wherein the linear transformation formula is:

[0192]

[0193] Among them, y1, y2, y3, y4, y5 and y6 are auxiliary variables, y1+y2+y3+y4+y5=1;

[0194] In this embodiment, in the multi-stage low-carbon planning model, formula (18) indicates that the economic benefits of the carbon trading market are a piecewise function, which needs to be linearly transformed. Six auxiliary variables y1, y2, y3, y4, y5, y6 are added, and the auxiliary variables are 0-1 variables. Formula (18) is transformed into the following formula:

[0195] C CO2,n =y1λE tr,n +y2λ(1+α)(E tr,n -d)+λd+y3λ(1+2α)(E tr,n -2d)+λ(2+α)d+y4λ(1+3α)(E tr,n -3d)+λ(3+3α)d+y5λ(1+4α)(E tr,n -4d)+λ(4+6α)d+y6λ(1+5α)(E tr -5d)+λ(5+10α)d (27)

[0196]

[0197] After linear transformation, the multi-stage low-carbon planning model of the new load park becomes a mixed integer programming model.

[0198] Step 1042: According to the objective function and planning constraints, the mixed integer programming model is optimally solved by a solver to obtain a planning investment and construction plan.

[0199] In this embodiment, computer languages ​​such as Python can be used to define the model variables, and the constraints and objective functions can be written into the program. The mixed integer programming model can be directly solved using the Gurobi commercial solver to obtain the optimal solution, which is the planning and construction plan.

[0200] In implementing the embodiment of the present invention, a data center energy consumption model is constructed according to the load spatiotemporal transfer characteristics of the data center of the new load park, and a resource energy consumption model is constructed according to the power resources of the new load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model; the carbon emissions of the new load park are calculated according to the carbon emission rights quota of the new load park, and the economic benefits of the carbon trading market are calculated according to the carbon emission rights quota and carbon emissions of the new load park; according to the data center energy consumption model, the resource energy consumption model, the carbon emissions of the new load park and the economic benefits of the carbon trading market, and with the minimization of the multi-stage comprehensive cost as the objective function, a multi-stage low-carbon planning model is constructed; the multi-stage low-carbon planning model is optimally solved to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages. A multi-stage low-carbon planning method for a new type of load park considering the temporal and spatial transfer characteristics of data center loads is proposed. The temporal and spatial transfer characteristics of data center loads are studied, and energy consumption models and carbon emission models of computing power and power resources are constructed. Combined with the constraints of carbon emission reduction targets, a multi-stage green and low-carbon planning model for a new type of load park with the goal of minimizing comprehensive costs is constructed, namely, a multi-stage low-carbon planning model. The multi-stage low-carbon planning model is solved to obtain a planning and construction plan. The corresponding low-carbon transformation computing power and power resources are invested and constructed in different planning stages of the new type of load park, which has a certain reference role for planners of the new type of load park, improves the resource utilization efficiency of the park, and reduces the carbon emissions of the park operation.

[0201] Embodiment 2

[0202] Accordingly, see Figure 3 , Figure 3 1 is a schematic diagram of a structure of a second embodiment of a multi-stage low-carbon planning system for a new type of load park provided by the present invention. The multi-stage low-carbon planning system for a new type of load park is used to implement a multi-stage low-carbon planning method for a new type of load park, such as Figure 3As shown, the multi-stage low-carbon planning system includes: an energy consumption model building module 301, a carbon emission calculation module 302, a planning model building module 303 and a model solving module 304;

[0203] The energy consumption model building module 301 is used to build a data center energy consumption model according to the load spatiotemporal transfer characteristics of the data center of the new load park, and to build a resource energy consumption model according to the power resources of the new load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model;

[0204] The carbon emission calculation module 302 is used to calculate the carbon emissions of the new load park according to the carbon emission quota of the new load park, and calculate the economic benefits of the carbon trading market according to the carbon emission quota and carbon emissions of the new load park;

[0205] The planning model building module 303 is used to build a multi-stage low-carbon planning model based on the data center energy consumption model, resource energy consumption model, carbon emissions of new load parks and economic benefits of the carbon trading market, and taking the multi-stage comprehensive cost minimization as the objective function;

[0206] The model solving module 304 is used to optimally solve the multi-stage low-carbon planning model to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages.

[0207] By implementing the embodiments of the present invention, a multi-stage low-carbon planning for a new type of load park that takes into account the temporal and spatial transfer characteristics of data center loads is carried out, the temporal and spatial transfer characteristics of data center loads are studied, the temporal and spatial transfer capabilities of data center loads in the new type of load park are analyzed, and energy consumption models (energy consumption models of computing power and power resources) and carbon emission models (carbon emissions and economic benefits of the carbon trading market) of park equipment are constructed. The economic benefits of the new type of load park in the carbon trading market are analyzed, and a multi-stage low-carbon planning method with the goal of minimizing the comprehensive cost is proposed in combination with the constraints of carbon emission reduction targets. A multi-stage green and low-carbon planning for a new type of load park with the goal of minimizing the comprehensive cost is constructed, which has a certain reference role for planners of new type of load parks.

[0208] The multi-stage low-carbon planning system of a new type of load park described above can implement the multi-stage low-carbon planning system of a new type of load park described above. The options in the above method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiments of this application can refer to the contents of the above method embodiment and will not be repeated in this embodiment.

[0209] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-stage low-carbon planning method for a load park, characterized in that: include: According to the load spatiotemporal transfer characteristics of the data center of the load park, a data center energy consumption model is constructed, and according to the power resources of the load park, a resource energy consumption model is constructed; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model; Calculating the carbon emissions of the load park according to the carbon emission rights quota of the load park, and calculating the economic benefits of the carbon trading market according to the carbon emission rights quota of the load park and the carbon emissions; According to the data center energy consumption model, the resource energy consumption model, the carbon emissions of the load park and the economic benefits of the carbon trading market, and taking the minimization of multi-stage comprehensive costs as the objective function, a multi-stage low-carbon planning model is constructed; The multi-stage low-carbon planning model is optimally solved to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages; The energy consumption model of a data center is constructed according to the load time-space transfer characteristics of the data center of the load park, specifically: According to the number of task requests transferred in time and space by the data center of the load park, the task request of the data center is obtained, specifically: Among them, v i,n,s,t represents the task request of data center i at time t in the sth scenario of the nth planning year, c i,n,s,t represents the number of delay-sensitive task requests of the data center i in the sth scenario at time t in the nth planning year, d i,n,s,t represents the number of delay-tolerant task requests of the data center i at the time t in the sth scenario in the nth planning year, and represents the number of delay-tolerant task requests that decrease and increase in the data center i at the sth scenario at time t in the nth planning year, w i|i′,n,s,t represents the number of task requests transferred from data center i to data center iˊ at time t in scenario s of the nth planning year, and I represents the total number of data centers; According to the conditions satisfied by the task request of the data center during the time-space transfer, the operation constraints of the data center are obtained; wherein the operation constraints of the data center include a maximum value constraint of active servers, a maximum response time constraint, and a maximum transmission capacity constraint; According to the no-load power of the server, the full-load power of the server, the number of servers, the task request of the data center and the operation constraints, a linear model of the data center server is constructed, specifically: in, represents the server power of the data center i at the sth scenario time t in the nth planning year, P n represents the no-load power of the server, P p represents the full load power of the server, m i,n,s,t represents the number of active servers in the data center i at the time t in the sth scenario in the nth planning year, and F is the maximum number of tasks processed by a single server; According to the data center server linear model and the data center energy efficiency coefficient, the data center energy consumption model is constructed, specifically: in, It represents the total power of the data center i at the sth scenario t in the nth planning year, and PUE represents the energy efficiency coefficient of the data center.

2. The multi-stage low-carbon planning method for a load park as claimed in claim 1, characterized in that: The operation constraints of the data center are obtained according to the conditions satisfied by the task request of the data center when performing the time-space transfer, specifically: According to the principle that the number of active servers does not exceed the number of servers to be built, the maximum active server limit constraint is constructed as follows: m i,n,s,t ≤X SRV,n Among them, X SRV,n The number of servers built for data center i in the nth planning year; According to the task request of the data center being executed within the maximum response time, the maximum response time constraint is constructed, specifically: 0≤1 / (m i,n,s,t F-v i,n,s,t )≤D Where D represents the delay limit of delay-sensitive load; According to the transfer of task requests between different data centers not exceeding the maximum communication transmission capacity between data centers, the maximum transmission capacity constraint is constructed, specifically: -In i|i′,max ≤in i|i′,s,t ≤in i|i′,max Among them, w i|i′,max It indicates the maximum number of task requests that can be transferred from data center i to data center iˊ.

3. The multi-stage low-carbon planning method for a load park according to claim 1, characterized in that: The resource energy consumption model is constructed according to the power resources of the load park, specifically: According to the power generation and ramp power of the thermal power unit, the thermal power unit model is constructed, specifically: in, represents the power generation cost of the thermal power unit at the sth scenario at time t in the nth planning year, α represents the power generation cost per unit power, represents the power generation capacity of the thermal power unit at the sth scenario at the time t in the nth planning year, and represents the upper and lower limits of the power generation capacity of the thermal power unit, and Indicates the upper and lower limits of the climbing power of the thermal power unit; According to the power and amount of energy storage, the energy storage model is constructed, specifically: In the formula, represents the operating cost of the energy storage at the sth scenario at time t in the nth planning year, β represents the unit power operating cost of the energy storage, represents the power of the energy storage at the sth scenario at time t in the nth planning year, represents the amount of energy stored in the energy storage at the sth scenario at time t in the nth planning year, Δt represents the scheduling step, and represent the upper and lower limits of the energy storage power respectively, and Respectively represent the upper limit and lower limit of the energy storage capacity; According to the power of the renewable energy generator set, the renewable energy generator set model is constructed, specifically: in, represents the power of the renewable energy generator set at the sth scenario at time t in the nth planning year, and Respectively represent the upper limit and lower limit of the power of the renewable energy generator set.

4. The multi-stage low-carbon planning method for a load park according to claim 1 or 3, characterized in that: The carbon emission amount of the load park is calculated according to the carbon emission quota of the load park, specifically: The carbon emission quota of the load park is calculated according to the power generation power, energy storage power and the total power of the thermal power unit, specifically: Among them, E′ Park,n , E′ e,buy,n , E′ MT,n , E′ ESS,n and E′ DC,n Respectively represent the carbon emission quotas of the load park, external power grid power purchase, thermal power unit, energy storage and data center in the nth planning year; δ′ e ,δ′ MT ,δ′ ESS and δ′ DC They represent the carbon emission quotas obtained by the power unit purchased by the upper power grid, the power unit produced by the thermal power unit, the power unit of energy storage, and the power unit of the data center respectively; Indicates the power purchased from the power grid by the load park at time t in the sth scenario in the nth planning year; represents the power generation capacity of the thermal power unit at the sth scenario at the time t in the nth planning year; represents the power of the energy storage at the sth scenario at the time t in the nth planning year; represents the total power of the data center i at the sth scenario at the time t in the nth planning year; According to the carbon emission quota of the load park, the carbon emission of the load park is calculated as follows: Among them, E Park,n , E e,buy,n , E MT,n , E ESS,n and E DC,n Respectively represent the carbon emissions of the load park, the external power grid power purchase, the thermal power unit, the energy storage and the data center in the nth planning year; δ e , δ MT , δ ESS and δ DC They respectively represent the unit power of electricity purchased by the superior power grid, the unit power produced by the thermal power unit, the unit power of energy storage, and the carbon emission quota obtained by the unit power of the data center.

5. The multi-stage low-carbon planning method for a load park as claimed in claim 4, characterized in that: The economic benefits of the carbon trading market are calculated based on the carbon emission quota of the load park and the carbon emissions, specifically: The carbon emission quota of the carbon trading market is calculated according to the carbon emission quota of the load park and the carbon emissions, specifically: AND tr,n =And Park,n -AND' Park,n Among them, E tr,n It represents the carbon emission quota of the carbon trading market in the nth planning year; The economic benefits of the carbon trading market are calculated based on the carbon emission quota, carbon trading base price, price growth rate and unit carbon emission interval length of the carbon trading market, specifically: Among them, C CO2,n is the economic benefit of the carbon trading market in the nth planned year, λ is the carbon trading base price, α is the price growth rate, and d is the length of the unit carbon emission interval.

6. The multi-stage low-carbon planning method for a load park as claimed in claim 5, characterized in that: According to the data center energy consumption model, the resource energy consumption model, the carbon emissions of the load park and the economic benefits of the carbon trading market, and taking the multi-stage comprehensive cost minimization as the objective function, a multi-stage low-carbon planning model is constructed, specifically: The data center energy consumption model and the resource energy consumption model are used as the operating cost of the load park, specifically: Among them, C ope,n represents the operating cost of the load park in the nth planning year, c e,n,s,t It represents the electricity purchase cost of the upper power grid at the t time in the sth scenario in the nth planning year; According to the equipment investment capacity and the cost per unit capacity, the equipment construction cost is obtained, which is: Among them, C constr,k represents the equipment construction cost in the kth planning stage; m represents the equipment type, M represents the equipment type set, C m,k represents the cost of investing in equipment m per unit capacity in the kth planning stage, X m,k represents the construction variable of equipment m in the kth planning stage, N m,k It represents the construction capacity of equipment m in the kth planning stage; The operating cost, the economic benefits of the carbon trading market and the equipment construction cost are taken as the multi-stage comprehensive cost, the minimum multi-stage comprehensive cost is taken as the objective function, and the carbon emission constraint, operating constraint and equipment construction quantity constraint are taken as planning constraints to construct a multi-stage low-carbon planning model, specifically: R n ＝(1+r) -n Among them, L RV A represents the residual value of the equipment at the end of the planning period. k is the starting year of the kth planning stage, N is the total planning period, K is the number of planning stages, r is the discount rate, R n and R N Respectively represent the A k The discount factors for the planning year, the nth planning year and the Nth planning year, where n represents the year; The residual value of the equipment at the end of the planning period is calculated based on the annual depreciation cost of the equipment, the equipment construction cost and the annual depreciation cost of the equipment, specifically: c dep,j =c constr,j (1-d j ) / N j Among them, M x is the number of remaining equipment at the end of the planning period, c constr,j represents the construction cost of the jth equipment, T j is the number of years of use of the jth equipment among the remaining equipment at the end of the planning period, c dep,j is the annual depreciation cost of the jth equipment, δ j is the net residual value rate of the jth equipment, N j is the life span of the jth device.

7. The multi-stage low-carbon planning method for a load park according to claim 6, characterized in that: The carbon emission constraints, operation constraints and equipment construction quantity constraints are used as planning constraints, specifically: According to the planned capacity upper limit of the energy equipment in the load park, the equipment construction quantity constraint is constructed, specifically: Among them, N m,k Plan the capacity of m types of energy equipment for the kth planning stage of the load park, Planning the capacity upper limit of m-type energy equipment in the k-th planning stage of the load park; Constructing the operation constraints according to the equipment operation constraints that need to be met by the thermal power units of the load park, the energy storage, the renewable energy power generation units, and the data center servers; According to the upper limit of the carbon emissions of the load park, the carbon emission constraint is constructed, specifically: Among them, E Park,n represents the carbon emissions of the load park in the nth planning year; E max,n It represents the upper limit of carbon emissions of the load park in the nth planning year; The equipment construction quantity constraint, the operation constraint and the carbon emission constraint are used as the planning constraint conditions.

8. The multi-stage low-carbon planning method for a load park as claimed in claim 7, characterized in that: The multi-stage low-carbon planning model is optimally solved to obtain a planning and construction plan, specifically: Auxiliary variables are established, and according to the auxiliary variables, the economic benefits of the carbon trading market in the multi-stage low-carbon planning model are linearly transformed, so that the multi-stage low-carbon planning model is transformed into a mixed integer programming model; wherein the formula for the linear transformation is: C CO2,n =y1λE tr,n +y2λ(1+α)(E tr,n -d)+λd+y3λ(1+2α)(E tr,n -2d)+λ(2+α)d+y4λ(1+3α)(E tr,n -3d)+λ(3+3α)d+y5λ(1+4α)(E tr,n -4d)+λ(4+6α)d+y6λ(1+5α)(E tr -5d)+λ(5+10a)d Wherein, y1, y2, y3, y4, y5 and y6 are the auxiliary variables respectively, y1+y2+y3+y4+y5=1; According to the objective function and the planning constraints, the mixed integer programming model is optimally solved by a solver to obtain the planning investment and construction plan.

9. A multi-stage low-carbon planning system for a load park, characterized in that: The multi-stage low-carbon planning system of the load park is used to implement the multi-stage low-carbon planning method of the load park as claimed in any one of claims 1 to 8, comprising: building an energy consumption model module, a carbon emission calculation module, building a planning model module and a model solving module; The energy consumption model building module is used to build a data center energy consumption model according to the load spatiotemporal transfer characteristics of the data center in the load park, and to build a resource energy consumption model according to the power resources of the load park; wherein the resource energy consumption model includes a thermal power unit model, an energy storage model and a renewable energy power generation unit model; The carbon emission calculation module is used to calculate the carbon emissions of the load park according to the carbon emission quota of the load park, and calculate the economic benefits of the carbon trading market according to the carbon emission quota of the load park and the carbon emissions; The planning model building module is used to build a multi-stage low-carbon planning model based on the data center energy consumption model, the resource energy consumption model, the carbon emissions of the load park and the economic benefits of the carbon trading market, and taking the minimization of the multi-stage comprehensive cost as the objective function; The model solving module is used to optimally solve the multi-stage low-carbon planning model to obtain a planning investment and construction plan; wherein the planning investment and construction plan includes computing power and power resources invested and constructed in different planning stages.

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